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      Who needs ‘lazy’ workers? Inactive workers act as a ‘reserve’ labor force replacing active workers, but inactive workers are not replaced when they are removed

      1 , * , 2 , 3

      PLoS ONE

      Public Library of Science

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          Abstract

          Social insect colonies are highly successful, self-organized complex systems. Surprisingly however, most social insect colonies contain large numbers of highly inactive workers. Although this may seem inefficient, it may be that inactive workers actually contribute to colony function. Indeed, the most commonly proposed explanation for inactive workers is that they form a ‘reserve’ labor force that becomes active when needed, thus helping mitigate the effects of colony workload fluctuations or worker loss. Thus, it may be that inactive workers facilitate colony flexibility and resilience. However, this idea has not been empirically confirmed. Here we test whether colonies of Temnothorax rugatulus ants replace highly active (spending large proportions of time on specific tasks) or highly inactive (spending large proportions of time completely immobile) workers when they are experimentally removed. We show that colonies maintained pre-removal activity levels even after active workers were removed, and that previously inactive workers became active subsequent to the removal of active workers. Conversely, when inactive workers were removed, inactivity levels decreased and remained lower post-removal. Thus, colonies seem to have mechanisms for maintaining a certain number of active workers, but not a set number of inactive workers. The rapid replacement (within 1 week) of active workers suggests that the tasks they perform, mainly foraging and brood care, are necessary for colony function on short timescales. Conversely, the lack of replacement of inactive workers even 2 weeks after their removal suggests that any potential functions they have, including being a ‘reserve’, are less important, or auxiliary, and do not need immediate recovery. Thus, inactive workers act as a reserve labor force and may still play a role as food stores for the colony, but a role in facilitating colony-wide communication is unlikely. Our results are consistent with the often cited, but never yet empirically supported hypothesis that inactive workers act as a pool of ‘reserve’ labor that may allow colonies to quickly take advantage of novel resources and to mitigate worker loss.

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          Most cited references 92

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          The large-scale organization of metabolic networks

          In a cell or microorganism the processes that generate mass, energy, information transfer, and cell fate specification are seamlessly integrated through a complex network of various cellular constituents and reactions. However, despite the key role these networks play in sustaining various cellular functions, their large-scale structure is essentially unknown. Here we present the first systematic comparative mathematical analysis of the metabolic networks of 43 organisms representing all three domains of life. We show that, despite significant variances in their individual constituents and pathways, these metabolic networks display the same topologic scaling properties demonstrating striking similarities to the inherent organization of complex non-biological systems. This suggests that the metabolic organization is not only identical for all living organisms, but complies with the design principles of robust and error-tolerant scale-free networks, and may represent a common blueprint for the large-scale organization of interactions among all cellular constituents.
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            Regulation of division of labor in insect societies.

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              Robotics. Programmable self-assembly in a thousand-robot swarm.

              Self-assembly enables nature to build complex forms, from multicellular organisms to complex animal structures such as flocks of birds, through the interaction of vast numbers of limited and unreliable individuals. Creating this ability in engineered systems poses challenges in the design of both algorithms and physical systems that can operate at such scales. We report a system that demonstrates programmable self-assembly of complex two-dimensional shapes with a thousand-robot swarm. This was enabled by creating autonomous robots designed to operate in large groups and to cooperate through local interactions and by developing a collective algorithm for shape formation that is highly robust to the variability and error characteristic of large-scale decentralized systems. This work advances the aim of creating artificial swarms with the capabilities of natural ones.
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                Author and article information

                Affiliations
                [1 ] Graduate Interdisciplinary Program in Entomology & Insect Science, University of Arizona, Biological Sciences West, 1041 East Lowell, Tucson, AZ, United States of America
                [2 ] Department of Zoology, University of Oxford, Oxford, United Kingdom
                [3 ] Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, United States of America
                University of Sheffield, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Contributors
                ORCID: http://orcid.org/0000-0002-9537-4763, Role: Conceptualization, Role: Data curation, Role: Formal analysis, Role: Investigation, Role: Methodology, Role: Project administration, Role: Writing – original draft, Role: Writing – review & editing
                Role: Conceptualization, Role: Methodology, Role: Writing – review & editing
                Role: Conceptualization, Role: Formal analysis, Role: Investigation, Role: Methodology, Role: Supervision, Role: Writing – original draft, Role: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                6 September 2017
                2017
                : 12
                : 9
                PONE-D-16-35177
                10.1371/journal.pone.0184074
                5587300
                28877229
                © 2017 Charbonneau et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Counts
                Figures: 5, Tables: 1, Pages: 20
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000154, Division of Integrative Organismal Systems;
                Award ID: IOS-1045239
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000154, Division of Integrative Organismal Systems;
                Award ID: IOS-0841756
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000153, Division of Biological Infrastructure;
                Award ID: DBI-1262292
                Award Recipient :
                Research supported through the GIDP-EIS and EEB Department at University of Arizona, as well as NSF grants no. IOS-1045239, IOS-0841756, and DBI-1262292 (to A.D.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Animals
                Invertebrates
                Arthropoda
                Insects
                Biology and Life Sciences
                Organisms
                Animals
                Invertebrates
                Arthropoda
                Insects
                Hymenoptera
                Ants
                Biology and Life Sciences
                Organisms
                Animals
                Invertebrates
                Arthropoda
                Insects
                Hymenoptera
                Bees
                Honey Bees
                Biology and Life Sciences
                Behavior
                Animal Behavior
                Foraging
                Biology and Life Sciences
                Zoology
                Animal Behavior
                Foraging
                Computer and Information Sciences
                Systems Science
                Complex Systems
                Physical Sciences
                Mathematics
                Systems Science
                Complex Systems
                Computer and Information Sciences
                Computer Networks
                Biology and Life Sciences
                Genetics
                Gene Identification and Analysis
                Genetic Networks
                Computer and Information Sciences
                Network Analysis
                Genetic Networks
                Biology and Life Sciences
                Physiology
                Physiological Processes
                Food Consumption
                Medicine and Health Sciences
                Physiology
                Physiological Processes
                Food Consumption
                Custom metadata
                All data files are available from the Dryad database (Provisional DOI: doi: 10.5061/dryad.77110).

                Uncategorized

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